Deep learning device for local processing classical chinese poetry and verse

ABSTRACT

A local processing device contains a bus, an input interface and at least one cellular neural networks (CNN) based integrated circuit (IC). The input interface is receiving a 2-D symbol representing a Chinese poetry or verse. The 2-D symbol is a matrix of N×N pixels of K-bit data that contains a “super-character”. The matrix is divided into M×M sub-matrices each containing (N/M)×(N/M) pixels. Each of the sub-matrices represents an ideogram. K, N and M are positive integers, and N is a multiple of M. CNN based IC is configured for understanding semantic meaning of the Chinese poetry or verse within the “super-character” contained in the 2-D symbol. The ideogram is created by embedded fonts of all of the characters contained in a corresponding phrase of the Chinese poetry or verse or is a pictogram representing artistic conception of each sentence of the Chinese poetry or verse.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part (CIP) of a co-pending U.S. patent application Ser. No. 15/709,220 for “Natural Language Processing Using A CNN Based Integrated Circuit” filed on Sep. 19, 2017, which is a CIP to a co-pending U.S. patent application Ser. No. 15/694,711 for “Natural Language Processing Via A Two-dimensional Symbol Having Multiple Ideograms Contained Therein” filed on Sep. 1, 2017, which is a CIP to a co-pending U.S. patent application Ser. No. 15/683,723 for “Two-dimensional Symbols For Facilitating Machine Learning Of Combined Meaning Of Multiple Ideograms Contained Therein” filed on Aug. 22, 2017, which claims priority from a U.S. Provisional Patent Application Ser. No. 62/541,081, entitled “Two-dimensional Symbol For Facilitating Machine Learning Of Natural Languages Having Logosyllabic Characters” filed on Aug. 3, 2017. All of which are hereby incorporated by reference in their entirety for all purposes.

FIELD

This document generally relates to the field of machine learning. More particularly the present document relates to deep learning devices for local processing classical Chinese poetry and verse using Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based Integrated Circuits (ICs).

BACKGROUND

An ideogram is a graphic symbol that represents an idea or concept. Some ideograms are comprehensible only by familiarity with prior convention; others convey their meaning through pictorial resemblance to a physical object.

Machine learning is an application of artificial intelligence. In machine learning, a computer or computing device is programmed to think like human beings so that the computer may be taught to learn on its own. The development of neural networks has been key to teaching computers to think and understand the world in the way human beings do.

Classical Chinese poetry is a combination of 5 by 4 Chinese characters, or 7 by 4 Chinese characters, which are formed in 4 phrases of 5 or 7 Chinese characters per phrase. Each classical Chinese poetry represents certain artistic conception and sentiment with complex context. To learn such high level of Chinese language, prior art approaches have been limited to recite a poem using a computer with search capability in a database storing classical Chinese poems and/or verses. For example, prior art approaches can produce an output of a following phrase in response to an input of a prior phrase in a particular poem or verse. None of the prior art approaches address the sentiment of a poem. Therefore, it would be desirable to have a novel local processing device that understands classical Chinese poetry and verse.

SUMMARY

This section is for the purpose of summarizing some aspects of the invention and to briefly introduce some preferred embodiments. Simplifications or omissions in this section as well as in the abstract and the title herein may be made to avoid obscuring the purpose of the section. Such simplifications or omissions are not intended to limit the scope of the invention.

Deep learning devices for classical Chinese poetry and verse are disclosed. According to one aspect of the disclosure, a deep learning device contains a bus, an input interface and at least one cellular neural networks (CNN) based integrated circuit (IC). The input interface is receiving a 2-D symbol representing a Chinese poetry or verse. The 2-D symbol is a matrix of N×N pixels of K-bit data that contains a “super-character”. The matrix is divided into M×M sub-matrices each containing (N/M)×(N/M) pixels. Each of the sub-matrices represents an ideogram. K, N and M are positive integers, and N is a multiple of M. CNN based IC is configured for understanding semantic meaning of the Chinese poetry or verse within the “super-character” contained in the 2-D symbol. The ideogram is created by embedded fonts of all of the characters contained in a corresponding phrase of the Chinese poetry or verse or is a pictogram representing artistic conception of each sentence of the Chinese poetry or verse.

According to another aspect, each of the at least one CNN based IC comprises a plurality of CNN processing engines operatively coupled to at least one input/output data bus, the plurality of CNN processing engines being connected in a loop with a clock-skew circuit. Each CNN processing engine includes a CNN processing block configured for simultaneously obtaining convolutional operations results using a corresponding portion of the “super-character” in the 2-D symbol and pre-trained filter coefficients in a deep learning model, a first set of memory buffers operatively coupling to the CNN processing block for storing the corresponding portion of the “super-character” and a second set of memory buffers operative coupling to the CNN processing block for storing the pre-trained filter coefficients.

Ideogram collection set includes, but is not limited to, pictograms, icons, logos, logosyllabic characters, punctuation marks, numerals, and special characters.

One of the objectives, features, and advantages of the invention is to use a CNN based integrated circuit having dedicated built-in logics for performing simultaneous convolutions such that the image processing technique (i.e., convolutional neural networks) for natural language processing to understand classical Chinese poetry and verse is conducted therein.

Other objects, features, and advantages of the invention will become apparent upon examining the following detailed description of an embodiment thereof, taken in conjunction with the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the invention will be better understood with regard to the following description, appended claims, and accompanying drawings as follows:

FIG. 1 is a diagram illustrating an example two-dimensional symbol comprising a matrix of N×N pixels of data that represents a “super-character” for facilitating machine learning of a combined meaning of multiple ideograms contained therein according to an embodiment of the invention;

FIGS. 2A-2B are diagrams showing example partition schemes for dividing the two-dimensional symbol of FIG. 1 in accordance with embodiments of the invention;

FIGS. 3A-3B show example ideograms in accordance with an embodiment of the invention;

FIG. 3C shows example pictograms containing western languages based on Latin letters in accordance with an embodiment of the invention;

FIG. 3D shows three respective basic color layers of an example ideogram in accordance with an embodiment of the invention;

FIG. 3E shows three related layers of an example ideogram for dictionary-like definition in accordance with an embodiment of the invention;

FIG. 4A is a block diagram illustrating an example Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based computing system for machine learning of a combined meaning of multiple ideograms contained in a two-dimensional symbol, according to one embodiment of the invention;

FIG. 4B is a block diagram illustrating an example CNN based integrated circuit for performing image processing based on convolutional neural networks, according to one embodiment of the invention;

FIG. 5A is a flowchart illustrating an example process of machine learning of written natural languages using a multi-layer two-dimensional symbol in accordance with an embodiment of the invention;

FIG. 5B is a schematic diagram showing an example natural language processing via a multi-layer two-dimensional symbol with image processing technique in accordance with an embodiment of the invention;

FIGS. 6A-6C are collectively a flowchart illustrating an example process of forming a two-dimensional symbol containing multiple ideograms from a string of natural language texts in accordance with an embodiment of the invention;

FIG. 7 is a schematic diagram showing an example image processing technique based on convolutional neural networks in accordance with an embodiment of the invention;

FIG. 8 is a diagram showing an example CNN processing engine in a CNN based integrated circuit, according to one embodiment of the invention;

FIG. 9 is a diagram showing an example imagery data region within the example CNN processing engine of FIG. 8, according to an embodiment of the invention;

FIGS. 10A-10C are diagrams showing three example pixel locations within the example imagery data region of FIG. 9, according to an embodiment of the invention;

FIG. 11 is a diagram illustrating an example data arrangement for performing 3×3 convolutions at a pixel location in the example CNN processing engine of FIG. 8, according to one embodiment of the invention;

FIGS. 12A-12B are diagrams showing two example 2×2 pooling operations according to an embodiment of the invention;

FIG. 13 is a diagram illustrating a 2×2 pooling operation of an imagery data in the example CNN processing engine of FIG. 8, according to one embodiment of the invention;

FIGS. 14A-14C are diagrams illustrating various examples of imagery data region within an input image, according to one embodiment of the invention;

FIG. 15 is a diagram showing a plurality of CNN processing engines connected as a loop via an example clock-skew circuit in accordance of an embodiment of the invention;

FIG. 16 is a flowchart illustrating an example process of natural language processing using a CNN based integrated circuit in accordance with an embodiment of the invention;

FIG. 17 is a flowchart illustrating an example process of achieving a trained convolutional neural networks model having bi-valued 3×3 filter kernels in accordance with an embodiment of the invention;

FIG. 18A is a diagram showing an example data conversion scheme;

FIG. 18B is a diagram showing an example filter kernel conversion scheme in accordance with the invention;

FIG. 19 is a functional diagram showing an example deep learning device for understanding semantic meanings of classical Chinese poetry and verse in accordance with an embodiment of the invention;

FIG. 20A is a diagram showing example schemes of creating “super-character” from classical Chinese poems in accordance with an embodiment of the invention;

FIG. 20B is a diagram showing an example scheme of creating “super-character” from classical Chinese verses in accordance with an embodiment of the invention; and

FIG. 21 is a diagram showing an example graphical representation of classical Chinese poem for creating a “super-character” in accordance with an embodiment of the invention.

DETAILED DESCRIPTIONS

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will become obvious to those skilled in the art that the invention may be practiced without these specific details. The descriptions and representations herein are the common means used by those experienced or skilled in the art to most effectively convey the substance of their work to others skilled in the art. In other instances, well-known methods, procedures, and components have not been described in detail to avoid unnecessarily obscuring aspects of the invention.

Reference herein to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Used herein, the terms “vertical”, “horizontal”, “diagonal”, “left”, “right”, “top”, “bottom”, “column”, “row”, “diagonally” are intended to provide relative positions for the purposes of description, and are not intended to designate an absolute frame of reference. Additionally, used herein, term “character” and “script” are used interchangeably.

Embodiments of the invention are discussed herein with reference to FIGS. 1-21. However, those skilled in the art will readily appreciate that the detailed description given herein with respect to these figures is for explanatory purposes as the invention extends beyond these limited embodiments.

Referring first to FIG. 1, it is shown a diagram showing an example two-dimensional symbol 100 for facilitating machine learning of a combined meaning of multiple ideograms contained therein. The two-dimensional symbol 100 comprises a matrix of N×N pixels (i.e., N columns by N rows) of data containing a “super-character”. Pixels are ordered with row first and column second as follows: (1,1), (1,2), (1,3), . . . (1,N), (2,1), . . . , (N,1), (N,N). N is a positive integer, for example in one embodiment, N is equal to 224.

“Super-character” represents at least one meaning, each formed with a specific combination of a plurality of ideograms. Since an ideogram can be represented in a certain size matrix of pixels, two-dimensional symbol 100 is divided into M×M sub-matrices. Each of the sub-matrices represents one ideogram, which is defined in an ideogram collection set by humans. “Super-character” contains a minimum of two and a maximum of M×M ideograms. Both N and M are positive integers, and N is preferably a multiple of M.

Shown in FIG. 2A, it is a first example partition scheme 210 of dividing a two-dimension symbol into M×M sub-matrices 212. M is equal to 4 in the first example partition scheme. Each of the M×M sub-matrices 212 contains (N/M)×(N/M) pixels. When N is equal to 224, each sub-matrix contains 56×56 pixels and there are 16 sub-matrices.

A second example partition scheme 220 of dividing a two-dimension symbol into M×M sub-matrices 222 is shown in FIG. 2B. M is equal to 8 in the second example partition scheme. Each of the M×M sub-matrices 222 contains (N/M)×(N/M) pixels. When N is equal to 224, each sub-matrix contains 28×28 pixels and there are 64 sub-matrices.

FIG. 3A shows example ideograms 301-304 that can be represented in a sub-matrix 222 (i.e., 28×28 pixels). For those having ordinary skill in the art would understand that the sub-matrix 212 having 56×56 pixels can also be adapted for representing these ideograms. The first example ideogram 301 is a pictogram representing an icon of a person riding a bicycle. The second example ideogram 302 is a logosyllabic script or character representing an example Chinese character. The third example ideogram 303 is a logosyllabic script or character representing an example Japanese character and the fourth example ideogram 304 is a logosyllabic script or character representing an example Korean character. Additionally, ideogram can also be punctuation marks, numerals or special characters. In another embodiment, pictogram may contain an icon of other images. Icon used herein in this document is defined by humans as a sign or representation that stands for its object by virtue of a resemblance or analogy to it.

FIG. 3B shows several example ideograms representing: a punctuation mark 311, a numeral 312 and a special character 313. Furthermore, pictogram may contain one or more words of western languages based on Latin letters, for example, English, Spanish, French, German, etc. FIG. 3C shows example pictograms containing western languages based on Latin letters. The first example pictogram 326 shows an English word “MALL”. The second example pictogram 327 shows a Latin letter “U” and the third example pictogram 328 shows English alphabet “Y”. Ideogram can be any one of them, as long as the ideogram is defined in the ideogram collection set by humans.

Only limited number of features of an ideogram can be represented using one single two-dimensional symbol. For example, features of an ideogram can be black and white when data of each pixel contains one-bit. Feature such as grayscale shades can be shown with data in each pixel containing more than one-bit.

Additional features are represented using two or more layers of an ideogram. In one embodiment, three respective basic color layers of an ideogram (i.e., red, green and blue) are used collectively for representing different colors in the ideogram. Data in each pixel of the two-dimensional symbol contains a K-bit binary number. K is a positive integer. In one embodiment, K is 5.

FIG. 3D shows three respective basic color layers of an example ideogram. Ideogram of a Chinese character are shown with red 331, green 332 and blue 333. With different combined intensity of the three basic colors, a number of color shades can be represented. Multiple color shades may exist within an ideogram.

In another embodiment, three related ideograms are used for representing other features such as a dictionary-like definition of a Chinese character shown in FIG. 3E. There are three layers for the example ideogram in FIG. 3E: the first layer 341 showing a Chinese logosyllabic character, the second layer 342 showing the Chinese “pinyin” pronunciation as “wang”, and the third layer 343 showing the meaning in English as “king”.

Ideogram collection set includes, but is not limited to, pictograms, icons, logos, logosyllabic characters, punctuation marks, numerals, special characters. Logosyllabic characters may contain one or more of Chinese characters, Japanese characters, Korean characters, etc.

In order to systematically include Chinese characters, a standard Chinese character set (e.g., GB18030) may be used as a start for the ideogram collection set. To include Japanese and Korean characters, CJK Unified Ideographs may be used. Other character sets for logosyllabic characters or scripts may also be used.

A specific combined meaning of ideograms contained in a “super-character” is a result of using image processing techniques in a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based computing system. Image processing techniques include, but are not limited to, convolutional neural networks, recurrent neural networks, etc.

“Super-character” represents a combined meaning of at least two ideograms out of a maximum of M×M ideograms. In one embodiment, a pictogram and a Chinese character are combined to form a specific meaning. In another embodiment, two or more Chinese characters are combined to form a meaning. In yet another embodiment, one Chinese character and a Korean character are combined to form a meaning. There is no restriction as to which two or more ideograms to be combined.

Ideograms contained in a two-dimensional symbol for forming “super-character” can be arbitrarily located. No specific order within the two-dimensional symbol is required. Ideograms can be arranged left to right, right to left, top to bottom, bottom to top, or diagonally.

Using written Chinese language as an example, combining two or more Chinese characters may result in a “super-character” including, but not limited to, phrases, idioms, proverbs, poems, sentences, paragraphs, written passages, articles (i.e., written works). In certain instances, the “super-character” may be in a particular area of the written Chinese language. The particular area may include, but is not limited to, certain folk stories, historic periods, specific background, etc.

Referring now to FIG. 4A, it is shown a block diagram illustrating an example CNN based computing system 400 configured for machine learning of a combined meaning of multiple ideograms contained in a two-dimensional symbol (e.g., the two-dimensional symbol 100).

The CNN based computing system 400 may be implemented on integrated circuits as a digital semi-conductor chip (e.g., a silicon substrate) and contains a controller 410, and a plurality of CNN processing units 402 a-402 b operatively coupled to at least one input/output (I/O) data bus 420. Controller 410 is configured to control various operations of the CNN processing units 402 a-402 b, which are connected in a loop with a clock-skew circuit.

In one embodiment, each of the CNN processing units 402 a-402 b is configured for processing imagery data, for example, two-dimensional symbol 100 of FIG. 1.

To store an ideogram collection set, it is required to have one or more storage units operatively coupled to the CNN based computing system 400. Storage units (not shown) can be located either inside or outside the CNN based computing system 400 based on well known techniques.

“Super-character” may contain more than one meanings in certain instances. “Super-character” can tolerate certain data errors that can be corrected with error-correction techniques. In other words, the pixels represent ideograms do not have to be exact. Data errors may be a result of different causes, for example, incorrect input data, data corruptions during data retrieval, etc.

In another embodiment, the CNN based computing system is a digital integrated circuit that can be extendable and scalable. For example, multiple copies of the digital integrated circuit may be implemented on a single semi-conductor chip as shown in FIG. 4B.

All of the CNN processing engines are identical. For illustration simplicity, only few (i.e., CNN processing engines 422 a-422 h, 432 a-432 h) are shown in FIG. 4B. The invention sets no limit to the number of CNN processing engines on a digital semi-conductor chip.

Each CNN processing engine 422 a-422 h, 432 a-432 h contains a CNN processing block 424, a first set of memory buffers 426 and a second set of memory buffers 428. The first set of memory buffers 426 is configured for receiving imagery data and for supplying the already received imagery data to the CNN processing block 424. The second set of memory buffers 428 is configured for storing filter coefficients and for supplying the already received filter coefficients to the CNN processing block 424. In general, the number of CNN processing engines on a chip is 2^(n), where n is an integer (i.e., 0, 1, 2, 3, . . . ). As shown in FIG. 4B, CNN processing engines 422 a-422 h are operatively coupled to a first input/output data bus 430 a while CNN processing engines 432 a-432 h are operatively coupled to a second input/output data bus 430 b. Each input/output data bus 430 a-430 b is configured for independently transmitting data (i.e., imagery data and filter coefficients). In one embodiment, the first and the second sets of memory buffers comprise random access memory (RAM), which can be a combination of one or more types, for example, Magnetic Random Access Memory, Static Random Access Memory, etc. Each of the first and the second sets are logically defined. In other words, respective sizes of the first and the second sets can be reconfigured to accommodate respective amounts of imagery data and filter coefficients.

The first and the second I/O data bus 430 a-430 b are shown here to connect the CNN processing engines 422 a-422 h, 432 a-432 h in a sequential scheme. In another embodiment, the at least one I/O data bus may have different connection scheme to the CNN processing engines to accomplish the same purpose of parallel data input and output for improving performance.

FIG. 5A is a flowchart illustrating an example process 500 of machine learning of written natural languages using a multi-layer two-dimensional symbol in accordance with an embodiment of the invention. Process 500 can be implemented in software as an application module installed in at least one computer system. Process 500 may also be implemented in hardware (e.g., integrated circuits). FIG. 5B is a schematic diagram showing example natural language processing via a multi-layer two-dimensional symbol with image process technique in accordance with an embodiment of the invention.

Process 500 starts at action 502 by receiving a string of natural language texts 510 in a first computing system 520 having at least one application module 522 installed thereon. The first computing system 520 can be a general computer capable of converting a string of natural language texts 510 to a multi-layer two-dimensional symbol 531 a-531 c (i.e., an image contained in a matrix of N×N pixels of data in multiple layers).

Next, at action 504, a multi-layer two-dimensional symbol 531 a-531 c containing M×M ideograms 532 (e.g., two-dimensional symbol 100 of FIG. 1) are formed from the received string 510 with the at least one application module 522 in the first computing system 520. M is a positive integer. Each two-dimensional symbol 531 a-531 c is a matrix of N×N pixels of data containing a “super-character”. The matrix is divided into M×M sub-matrices representing respective M×M ideograms. “Super-character” represents a meaning formed from a specific combination of a plurality of ideograms contained in the multi-layer two-dimensional symbol 531 a-531 c. M and N are positive integers, and N is preferably a multiple of M. More details of forming the multi-layer two-dimensional symbol are shown in FIG. 6 and corresponding descriptions.

Finally, at action 506, the meaning of the “super-character” contained in the multi-layer two-dimensional symbol 531 a-531 c is learned in a second computing system 540 by using an image processing technique 538 to classify the multi-layer two-dimensional symbol 531 a-531 c, which is formed in the first computing system 520 and transmitted to the second computing system 540. The second computing system 540 is capable of image processing of imagery data such as the multi-layer two-dimensional symbol 531 a-531 c.

Transmitting the multi-layer 2-D symbol 531 a-531 c can be performed with many well-known manners, for example, through a network either wired or wireless.

In one embodiment, the first computing system 520 and the second computing system 540 are the same computing system (not shown).

In yet another embodiment, the first computing system 520 is a general computing system while the second computing system 540 is a CNN based computing system 400 implemented as integrated circuits on a semi-conductor chip shown in FIG. 4A.

The image processing technique 538 includes predefining a set of categories 542 (e.g., “Category-1”, “Category-2”, . . . “Category-X” shown in FIG. 5B). As a result of performing the image processing technique 538, respective probabilities 544 of the categories are determined for associating each of the predefined categories 542 with the meaning of the “super-character”. In the example shown in FIG. 5B, the highest probability of 88.08 percent is shown for “Category-2”. In other words, the multi-layer two-dimensional symbol 531 a-531 c contains a “super-character” whose meaning has a probability of 88.08 percent associated with “Category-2” amongst all the predefined categories 544.

In another embodiment, predefined categories contain commands that can activate a sequential instructions on a smart electronic device (e.g., computing device, smart phone, smart appliance, etc.). For example, a multi-layer two-dimensional symbol is formed from a string of 16 logosyllabic Chinese characters. “Super-character” in the multi-layer 2-D symbol thus contains 16 ideograms in three colors (i.e., red, green and blue). After applying image processing technique to imagery data of the 2-D symbol, a series of commands for smart electronic devices is obtained by classifying the imagery data with a set of predefined commands. In this particular example, the meaning of the 16 logosyllabic Chinese characters is “open an online map and find the nearest route to fast food”. The series of commands may be as follows:

1) open “online map”

2) search “fast food near me”

3) enter

4) click “Go”

In one embodiment, image processing technique 538 comprises example convolutional neural networks shown in FIG. 7. In another embodiment, image processing technique 538 comprises support vector machine (SVM) with manual feature engineering on images of specific set of logosyllabic characters (e.g., Chinese characters).

FIGS. 6A-6C are collectively a flowchart illustrating an example process 600 of forming a two-dimensional (2-D) symbol containing multiple ideograms from a string of natural language texts in accordance with an embodiment of the invention. Process 600 can be implemented in software as an application module installed in a computer system. Process 600 can also be implemented in hardware (e.g., integrated circuits).

Process 600 starts at action 602 by receiving a string of natural language texts in a computing system having at least one application module installed thereon. An example application module is a software that contains instructions for the computing system to perform the actions and decisions set forth in process 600. The string of natural language texts may include, but are not necessarily limited to, logosyllabic characters, numerals, special characters, western languages based on Latin letters, etc. The string of natural language texts can be inputted to the computing system via various well-known manners, for example, keyboard, mouse, voice-to-text, etc.

Next, at action 604, a size of the received string of natural language texts is determined. Then at decision 610, it is determined whether the size is greater than M×M (i.e., the maximum number of ideograms in the two-dimensional symbol). In one embodiment, M is 4 and M×M is therefore 16. In another embodiment, M is 8 and M×M is then 64.

When decision 610 is true, the received string is too large to be fit into the 2-D symbol and must be first reduced in accordance with at least one language text reduction scheme described below.

Process 600 follows the ‘yes’ branch to action 611. Process 600 attempts to identify an unimportant text in the string according to at least one relevant grammar based rule. The relevant grammar based rule is associated with the received string of natural language texts. For example, when the natural language is Chinese, the relevant grammar is the Chinese grammar. Next, at decision 612, it is determined whether an unimportant text is identified or not. If ‘yes’, at action 613, the identified unimportant text is deleted from the string, and therefore the size of the string is reduced by one. At decision 614, the size of the string is determined if it is equal to M×M. If not, process 600 goes back to repeat the loop of action 611, decision 612, action 613 and decision 614. If decision 614 is true, process 600 ends after performing action 618, in which a multi-layer 2-D symbol is formed by converting the string in its current state (i.e., may have one or more unimportant texts deleted).

During the aforementioned loop 611-614, if there is no more unimportant text in the received string, decision 612 becomes ‘no’. Process 600 moves to action 616 to further reduce the size of the string to M×M via a randomized text reduction scheme, which can be truncation or arbitrary selection. At action 618, a multi-layer 2-D symbol is formed by converting the string in its current state. Process 600 ends thereafter.

The randomized text reduction scheme and the aforementioned scheme of deleting unimportant text are referred to as the at least one language text reduction scheme.

Referring back to decision 610, if it is false, process 600 follows the ‘no’ branch to decision 620. If the size of the received string is equal to M×M, decision 620 is true. Process 600 moves to action 622, in which a multi-layer 2-D symbol is formed by converting the received string. Process 600 ends thereafter.

If decision 620 is false (i.e., the size of the received string is less than M×M), process 600 moves to another decision 630, in which it is determined whether a padding operation of the 2-D symbol is desired. If ‘yes’, at action 632, the string is padded with at least one text to increase the size of the string to M×M in accordance with at least one language text increase scheme. In other words, at least one text is added to the string such that the size of the string is equal to M×M. In one embodiment, the language text increase scheme requires one or more key texts be identified from the received string first. Then one or more identified key texts are repeatedly appended to the received string. In another embodiment, the language text increase scheme requires one or more texts from the receiving string be repeatedly appended to the string. Next, action 622 is performed to form a multi-layer 2-D symbol by converting the padded string (i.e., the received string plus at least one additional text). Process 600 ends thereafter.

If decision 630 is false, process 600 ends after performing action 634. A multi-layer 2-D symbol is formed by converting the received string, which has a size less than M×M. As a result, the 2-D symbol contains at least one empty space. In one embodiment, the multi-layer two-dimensional symbol 531 a-531 c contains three layers for red, green and blue hues. Each pixel in each layer of the two-dimension symbol contains K-bit. In one embodiment, K=8 for supporting true color, which contains 256 shades of red, green and blue. In another embodiment, K=5 for a reduced color map having 32 shades of red, green and blue.

FIG. 7 is a schematic diagram showing an example image processing technique based on convolutional neural networks in accordance with an embodiment of the invention.

Based on convolutional neural networks, a multi-layer two-dimensional symbol 711 a-711 c as input imagery data is processed with convolutions using a first set of filters or weights 720. Since the imagery data of the 2-D symbol 711 a-711 c is larger than the filters 720. Each corresponding overlapped sub-region 715 of the imagery data is processed. After the convolutional results are obtained, activation may be conducted before a first pooling operation 730. In one embodiment, activation is achieved with rectification performed in a rectified linear unit (ReLU). As a result of the first pooling operation 730, the imagery data is reduced to a reduced set of imagery data 731 a-731 c. For 2×2 pooling, the reduced set of imagery data is reduced by a factor of 4 from the previous set.

The previous convolution-to-pooling procedure is repeated. The reduced set of imagery data 731 a-731 c is then processed with convolutions using a second set of filters 740. Similarly, each overlapped sub-region 735 is processed. Another activation can be conducted before a second pooling operation 740. The convolution-to-pooling procedures are repeated for several layers and finally connected to a Fully Connected (FC) Layers 760. In image classification, respective probabilities 544 of predefined categories 542 can be computed in FC Layers 760.

This repeated convolution-to-pooling procedure is trained using a labeled dataset or database. For image classification, the dataset contains the predefined categories. A particular set of filters, activation and pooling can be tuned and obtained before classifying an imagery data, for example, a specific combination of filter types, number of filters, order of filters, pooling types, and/or when to perform activation. In one embodiment, the imagery data is the multi-layer two-dimensional symbol 711 a-711 c, which is form from a string of natural language texts.

In one embodiment, convolutional neural networks are based on a Visual Geometry Group (VGG16) architecture neural nets.

More details of a CNN processing engine 802 in a CNN based integrated circuit are shown in FIG. 8. A CNN processing block 804 contains digital circuitry that simultaneously obtains Z×Z convolution operations results by performing 3×3 convolutions at Z×Z pixel locations using imagery data of a (Z+2)-pixel by (Z+2)-pixel region and corresponding filter coefficients from the respective memory buffers. The (Z+2)-pixel by (Z+2)-pixel region is formed with the Z×Z pixel locations as an Z-pixel by Z-pixel central portion plus a one-pixel border surrounding the central portion. Z is a positive integer. In one embodiment, Z equals to 14 and therefore, (Z+2) equals to 16, Z×Z equals to 14×14=196, and Z/2 equals 7.

FIG. 9 is a diagram showing a diagram representing (Z+2)-pixel by (Z+2)-pixel region 910 with a central portion of Z×Z pixel locations 920 used in the CNN processing engine 802.

In order to achieve faster computations, few computational performance improvement techniques have been used and implemented in the CNN processing block 804. In one embodiment, representation of imagery data uses as few bits as practical (e.g., 5-bit representation). In another embodiment, each filter coefficient is represented as an integer with a radix point. Similarly, the integer representing the filter coefficient uses as few bits as practical (e.g., 12-bit representation). As a result, 3×3 convolutions can then be performed using fixed-point arithmetic for faster computations.

Each 3×3 convolution produces one convolution operations result, Out(m, n), based on the following formula:

$\begin{matrix} {{{Out}\left( {m,n} \right)} = {{\sum\limits_{{1 \leq i},{j \leq 3}}{{{In}\left( {m,n,i,j} \right)} \times {C\left( {i,j} \right)}}} - b}} & (1) \end{matrix}$

where:

-   -   m, n are corresponding row and column numbers for identifying         which imagery data (pixel) within the (Z+2)-pixel by (Z+2)-pixel         region the convolution is performed;     -   In(m,n,i,j) is a 3-pixel by 3-pixel area centered at pixel         location (m, n) within the region;     -   C(i, j) represents one of the nine weight coefficients C(3×3),         each corresponds to one of the 3-pixel by 3-pixel area;     -   b represents an offset or bias coefficient; and     -   i,j are indices of weight coefficients C(i, j).

Each CNN processing block 804 produces Z×Z convolution operations results simultaneously and, all CNN processing engines perform simultaneous operations. In one embodiment, the 3×3 weight or filter coefficients are each 12-bit while the offset or bias coefficient is 16-bit or 18-bit.

FIGS. 10A-10C show three different examples of the Z×Z pixel locations. The first pixel location 1031 shown in FIG. 10A is in the center of a 3-pixel by 3-pixel area within the (Z+2)-pixel by (Z+2)-pixel region at the upper left corner. The second pixel location 1032 shown in FIG. 10B is one pixel data shift to the right of the first pixel location 1031. The third pixel location 1033 shown in FIG. 10C is a typical example pixel location. Z×Z pixel locations contain multiple overlapping 3-pixel by 3-pixel areas within the (Z+2)-pixel by (Z+2)-pixel region.

To perform 3×3 convolutions at each sampling location, an example data arrangement is shown in FIG. 11. Imagery data (i.e., In(3×3)) and filter coefficients (i.e., weight coefficients C(3×3) and an offset coefficient b) are fed into an example CNN 3×3 circuitry 1100. After 3×3 convolutions operation in accordance with Formula (1), one output result (i.e., Out(1×1)) is produced. At each sampling location, the imagery data In(3×3) is centered at pixel coordinates (m, n) 1105 with eight immediate neighbor pixels 1101-1104, 1106-1109.

Imagery data are stored in a first set of memory buffers 806, while filter coefficients are stored in a second set of memory buffers 808. Both imagery data and filter coefficients are fed to the CNN block 804 at each clock of the digital integrated circuit. Filter coefficients (i.e., C(3×3) and b) are fed into the CNN processing block 804 directly from the second set of memory buffers 808. However, imagery data are fed into the CNN processing block 804 via a multiplexer MUX 805 from the first set of memory buffers 806. Multiplexer 805 selects imagery data from the first set of memory buffers based on a clock signal (e.g., pulse 812).

Otherwise, multiplexer MUX 805 selects imagery data from a first neighbor CNN processing engine (from the left side of FIG. 8 not shown) through a clock-skew circuit 820.

At the same time, a copy of the imagery data fed into the CNN processing block 804 is sent to a second neighbor CNN processing engine (to the right side of FIG. 8 not shown) via the clock-skew circuit 820. Clock-skew circuit 820 can be achieved with known techniques (e.g., a D flip-flop 822).

After 3×3 convolutions for each group of imagery data are performed for predefined number of filter coefficients, convolution operations results Out(m, n) are sent to the first set of memory buffers via another multiplex MUX 807 based on another clock signal (e.g., pulse 811). An example clock cycle 810 is drawn for demonstrating the time relationship between pulse 811 and pulse 812. As shown pulse 811 is one clock before pulse 812, as a result, the 3×3 convolution operations results are stored into the first set of memory buffers after a particular block of imagery data has been processed by all CNN processing engines through the clock-skew circuit 820.

After the convolution operations result Out(m, n) is obtained from Formula (1), activation procedure may be performed. Any convolution operations result, Out(m, n), less than zero (i.e., negative value) is set to zero. In other words, only positive value of output results are kept. For example, positive output value 10.5 retains as 10.5 while −2.3 becomes 0. Activation causes non-linearity in the CNN based integrated circuits.

If a 2×2 pooling operation is required, the Z×Z output results are reduced to (Z/2)×(Z/2). In order to store the (Z/2)×(Z/2) output results in corresponding locations in the first set of memory buffers, additional bookkeeping techniques are required to track proper memory addresses such that four (Z/2)×(Z/2) output results can be processed in one CNN processing engine.

To demonstrate a 2×2 pooling operation, FIG. 12A is a diagram graphically showing first example output results of a 2-pixel by 2-pixel block being reduced to a single value 10.5, which is the largest value of the four output results. The technique shown in FIG. 12A is referred to as “max pooling”. When the average value 4.6 of the four output results is used for the single value shown in FIG. 12B, it is referred to as “average pooling”. There are other pooling operations, for example, “mixed max average pooling” which is a combination of “max pooling” and “average pooling”. The main goal of the pooling operation is to reduce the size of the imagery data being processed. FIG. 13 is a diagram illustrating Z×Z pixel locations, through a 2×2 pooling operation, being reduced to (Z/2)×(Z/2) locations, which is one fourth of the original size.

An input image generally contains a large amount of imagery data. In order to perform image processing operations, an example input image 1400 (e.g., a two-dimensional symbol 100 of FIG. 1) is partitioned into Z-pixel by Z-pixel blocks 1411-1412 as shown in FIG. 14A. Imagery data associated with each of these Z-pixel by Z-pixel blocks is then fed into respective CNN processing engines. At each of the Z×Z pixel locations in a particular Z-pixel by Z-pixel block, 3×3 convolutions are simultaneously performed in the corresponding CNN processing block.

Although the invention does not require specific characteristic dimension of an input image, the input image may be required to resize to fit into a predefined characteristic dimension for certain image processing procedures. In an embodiment, a square shape with (2^(L)×Z)-pixel by (2^(L)×Z)-pixel is required. L is a positive integer (e.g., 1, 2, 3, 4, etc.). When Z equals 14 and L equals 4, the characteristic dimension is 224. In another embodiment, the input image is a rectangular shape with dimensions of (2^(I)×Z)-pixel and (2^(J)×Z)-pixel, where I and J are positive integers.

In order to properly perform 3×3 convolutions at pixel locations around the border of a Z-pixel by Z-pixel block, additional imagery data from neighboring blocks are required. FIG. 14B shows a typical Z-pixel by Z-pixel block 1420 (bordered with dotted lines) within a (Z+2)-pixel by (Z+2)-pixel region 1430. The (Z+2)-pixel by (Z+2)-pixel region is formed by a central portion of Z-pixel by Z-pixel from the current block, and four edges (i.e., top, right, bottom and left) and four corners (i.e., top-left, top-right, bottom-right and bottom-left) from corresponding neighboring blocks.

FIG. 14C shows two example Z-pixel by Z-pixel blocks 1422-1424 and respective associated (Z+2)-pixel by (Z+2)-pixel regions 1432-1434. These two example blocks 1422-1424 are located along the perimeter of the input image. The first example Z-pixel by Z-pixel block 1422 is located at top-left corner, therefore, the first example block 1422 has neighbors for two edges and one corner. Value “0”s are used for the two edges and three corners without neighbors (shown as shaded area) in the associated (Z+2)-pixel by (Z+2)-pixel region 1432 for forming imagery data. Similarly, the associated (Z+2)-pixel by (Z+2)-pixel region 1434 of the second example block 1424 requires “0”s be used for the top edge and two top corners. Other blocks along the perimeter of the input image are treated similarly. In other words, for the purpose to perform 3×3 convolutions at each pixel of the input image, a layer of zeros (“0” s) is added outside of the perimeter of the input image. This can be achieved with many well-known techniques. For example, default values of the first set of memory buffers are set to zero. If no imagery data is filled in from the neighboring blocks, those edges and corners would contain zeros.

When more than one CNN processing engine is configured on the integrated circuit. The CNN processing engine is connected to first and second neighbor CNN processing engines via a clock-skew circuit. For illustration simplicity, only CNN processing block and memory buffers for imagery data are shown. An example clock-skew circuit 1540 for a group of example CNN processing engines are shown in FIG. 15.

CNN processing engines connected via the second example clock-skew circuit 1540 to form a loop. In other words, each CNN processing engine sends its own imagery data to a first neighbor and, at the same time, receives a second neighbor's imagery data. Clock-skew circuit 1540 can be achieved with well-known manners. For example, each CNN processing engine is connected with a D flip-flop 1542.

Referring now to FIG. 16, it is a flowchart illustrating an example process 1600 of natural language processing using a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based integrated circuit.

Process 1600 starts at action 1602 by receiving a string of written natural language texts in a computing system (e.g., a computer with multiple processing units). At action 1604, a multi-layer two-dimension (2-D) symbol is formed from the received string according to a set of 2-D symbol creation rules. The 2-D symbol contains a “super-character” representing a meaning formed from a specific combination of a plurality of ideograms contained in the 2-D symbol.

Details of an example multi-layer 2-D symbol 100 are described and shown in FIG. 1 and FIGS. 2A-2B. In order to accommodate a CNN based integrated circuit (e.g., example CNN based Integrated circuit 400 shown in FIGS. 4A-4B), each of the N×N pixel contains K-bit data, where K is a positive integer. In one embodiment, K is 5.

FIG. 18A is a diagram showing an example data conversion scheme for converting an imagery data (e.g., 2-D symbol) from 8-bit [0-255] to 5-bit [0-31] per pixel. For example, bits 0-7 becomes 0, bits 8-15 becomes 1, etc.

Next, at action 1606, the meaning of the “super-character” is learned by classifying the 2-D symbol via a trained convolutional neural networks model having bi-valued 3×3 filter kernels in the CNN based integrated circuit.

A trained convolutional neural networks model is achieved with an example set of operations 1700 shown in FIG. 17. At action 1702, a convolutional neural networks model is first obtained by training the convolutional neural networks model based on image classification of a labeled dataset, which contains a sufficiently large number of multi-layer 2-D symbols. For example, there are at least 4,000 2-D symbols for each category. In other words, each 2-D symbol in the labeled dataset is associated with a category to be classified. The convolutional neural networks model includes multiple ordered filter groups (e.g., each filter group corresponds to a convolutional layer in the convolutional neural networks model). Each filter in the multiple ordered filter groups contains a standard 3×3 filter kernel (i.e., nine coefficients in floating point number format (e.g., standard 3×3 filter kernel 1810 in FIG. 18B)). Each of the nine coefficients can be any negative or positive real number (i.e., a number with fraction). The initial convolutional neural networks model may be obtained from many different frameworks including, but not limited to, M×net, caffe, tensorflow, etc.

Then, at action 1704, the convolutional neural networks model is modified by converting respective standard 3×3 filter kernels 1810 to corresponding bi-valued 3×3 filter kernels 1820 of a currently-processed filter group in the multiple ordered filter groups based on a set of kernel conversion schemes. In one embodiment, each of the nine coefficients C(i,j) in the corresponding bi-valued 3×3 filter kernel 1820 is assigned a value ‘A’ which equals to the average of absolute coefficient values multiplied by the sign of corresponding coefficients in the standard 3×3 filter kernel 1810 shown in following formula:

$\begin{matrix} {A = {\sum\limits_{{1 \leq i},{j \leq 3}}{{{C\left( {i,j} \right)}}/9}}} & (2) \end{matrix}$

Filter groups are converted one at a time in the order defined in the multiple ordered filter groups. In certain situation, two consecutive filter groups are optionally combined such that the training of the convolutional neural networks model is more efficient.

Next, at action 1706, the modified convolutional neural networks model is retrained until a desired convergence criterion is met or achieved. There are a number of well known convergence criteria including, but not limited to, completing a predefined number of retraining operation, converging of accuracy loss due to filter kernel conversion, etc. In one embodiment, all filter groups including those already converted in previous retraining operations can be changed or altered for fine tuning. In another embodiment, the already converted filter groups are frozen or unaltered during the retraining operation of the currently-processed filter group.

Process 1700 moves to decision 1708, it is determined whether there is another unconverted filter group. If ‘yes’, process 1700 moves back to repeat actions 1704-1706 until all filter groups have been converted. Decision 1708 becomes ‘no’ thereafter. At action 1710, coefficients of bi-valued 3×3 filter kernels in all filter groups are transformed from a floating point number format to a fixed point number format to accommodate the data structure required in the CNN based integrated circuit. Furthermore, the fixed point number is implemented as reconfigurable circuits in the CNN based integrated circuit. In one embodiment, the coefficients are implemented using 12-bit fixed point number format.

Referring now to FIG. 19, it is shown an example deep learning device 1900 for understanding semantic meanings of classical Chinese poetry and verse. The device 1900 contains a bus 1910, at least one CNN based integrated circuit (IC) 1902, an input interface 1916, a display unit 1918, a storage 1920, a memory 1904, and a processing unit 1912.

Input interface 1916 (e.g., camera, scanner, touch screen, tablet pen, etc.) is operatively coupled to the bus 1910 for receiving a two-dimensional (2-D) symbol representing a Chinese poetry or verse. The 2-D symbol contains a matrix of N×N pixels of K-bit data that contains a “super-character” (e.g., 2-D symbol 100 of FIG. 1). The matrix is divided into M×M sub-matrices each containing (N/M)×(N/M) pixels. Each of the sub-matrices represents an ideogram. K, N and M are positive integers, and N is a multiple of M. Storage 1920 is used for storing coefficients when power is discounted from the deep learning device 1900. Example storage 1920 can be hard disks, non-volatile memory, and alike.

The CNN based IC 1902 is configured for understanding semantic meaning of the Chinese poetry or verse within the “super-character” contained in the 2-D symbol. An example CNN based IC 400 is shown in FIGS. 4A-4B, which is an application specific IC for performing simultaneous convolutional operations in a deep learning model.

The memory 1904 e.g., Dynamic Random Access Memory or other suitable alternative storage) can be configured for at least storing weight or filter coefficients of fully-connected (FC) layers. The memory 1904 may also be used as buffer for loading weight or filter coefficients into the CNN based IC 1902.

The processing unit 1912 (e.g., computer central processing unit, computer graphics processing unit. etc.) acts like a controller for carrying out a deep learning model (e.g., convolutional layers, FC layers, pooling operations, activation operations, etc.). The processing unit 1912 also controls input interface 1916 to obtain an input imagery data (i.e., 2-D symbol containing “super-character”). The input imagery data captured by a camera may require certain conversion to a suitable format that can be processed by the CNN based IC 1902 (e.g., a 224-pixel×224-pixel).

The display unit 1918 (e.g., display screen) displays the result of image classification based on the deep learning model. In other words, the CNN based IC 1902 is used for extracting features out of the imagery data. The processing unit 1912 performs computations of FC layers to determine which category (e.g., sentiment) of the Chinese poem embedded in the “super-character”. The deep learning device 1900 is configured for local processing Chinese poetry and verse.

FIG. 20A shows two example forms of classical Chinese poetry: the first form 2010 of 4 phrases with 5 characters per phrase and the second form 2020 of 4 phrases with 7 characters per phrase. In order to create a “super-character” within a 2-D symbol, the first form 2010 of classical Chinese poetry is converted to a 5×5 symbol 2012 (i.e., a 2-D symbol containing 25 ideograms) and the second form 2020 is converted to a 6×6 symbol 2022 (i.e., a 2-D symbol containing 36 ideograms). Each ideogram within the “super-character” corresponds to one Chinese character. One example scheme to create “super-character” is to embed the Chinese characters therein using pre-defined font. In another embodiment, a graphic image of the entire poem is captured with an input device 1916. The 2-D graphic image can then be converted to a format suitable for the CNN based IC 1902 to process.

For different sized rectangular poems (i.e., different number of characters per phrase), each individual phrase of a poem can be fit into a “super-character”, which leaves a rectangle of broadcasted words after, ending up with a square “super-character”. For poems that do not depend on the meaning of each individual phrase, those poems can be converted into a long sentence placing individual characters one after another in descending order.

As for classic Chinese verses, one specific example is shown in FIG. 20B. The classical Chinese verse contains 378 phrases 2030 a-2030 p with 3 characters per phrase. The classical Chinese verse 2030 a-2030 p can be converted into respective 2×2 symbols 2032 a-2032p. In the examples shown in FIGS. 20A-20B, there are blank spots or locations or ideograms in each “super-character”. These blank locations are not limited the example shown by the invention. For example, the phrase in the 2×2 symbols 2032 a-2032 p can be randomly scrambled to different sequence to be used in conjunction with the correct sequenced phrase as training dataset.

Another aspect is to perform data augmentation, which can be implemented by training with a few words missing in a poem to improve robustness, and/or substitute certain Chinese character in synonyms in order to learn different word banks. Scrambling of each individual phrase can also be implemented with “super-character”.

In an alternative embodiment, instead of Chinese character as an input, the Chinese poems can be represented as a series of pictures 2110 (i.e., Scene-1, Scene-2, Scene-3 and Scene-4) shown in FIG. 21. These four pictures can be related to artistic conception of respective phrases in the poem. These pictures may illustrate a specific thing mentioned in respective phrases in the poem. The picture can be any encoded image, for example, a single blue color image may represent sad mood.

In yet another embodiment, when input is a sand drawing art, other forms of multiple pictures, or even videos, some of the images can be sampled as graphic symbols or ideograms to form a “super-character” and output a corresponding poem.

Instead of reciting a following phrase of a poem in response to an input phrase, the deep learning model using “super-character” that contains classical Chinese poetry can output the sentiment of the phrase. Once a poem or verse is enclosed in a “super-character”, the 2-D symbol that contains the “super-character” is a 2-D imagery data that can be classified with a deep learning model substantially similar to the scheme described in FIGS. 5A-5B and FIG. 7. In one embodiment, sentiment analysis of a poem is achieved by training a deep learning model with a set of classical Chinese poems (e.g., 300 poems of Tang dynasty 618-907 A.D.) each encoded in “super-character” with one of the sentiments (e.g., homesickness, rural life, patriotism, and alike) as category. Once trained, the deep learning model would understand the sentiment (i.e., semantic meaning) of a new poem written in the form of classical Chinese poetry.

For an input phrase containing incorrect Chinese character or missing character, the deep learning model using “super-character” that contains classical Chinese poetry can be trained for a number of different tasks, including but not limited to, outputting the following phrase, outputting a picture representing artistic conception of the input phrase, translating the input phrase into plain language, and giving an interpretation or detailed description of the input phrase.

In yet another embodiment, a set of Chinese last name (i.e., The Hundred Family Surnames from Song Dynasty 960-1279 A.D.) is used as a training database, a correct pronunciation is the output to any input of the Chinese last name. It is noted that the Hundred Family Surnames actually contains 504 names.

Although the invention has been described with reference to specific embodiments thereof, these embodiments are merely illustrative, and not restrictive of, the invention. Various modifications or changes to the specifically disclosed example embodiments will be suggested to persons skilled in the art. For example, whereas the two-dimensional symbol has been described and shown with a specific example of a matrix of 224×224 pixels, other sizes may be used for achieving substantially similar objections of the invention. Additionally, whereas a particular scheme of using pictures/scenes as input has been shown and described, other schemes may be used for achieving substantially similar objectives of the invention, for example, using more or less than four scenes. Moreover, few example ideograms have been shown and described, other ideograms may be used for achieving substantially similar objectives of the invention. Finally, whereas one type of bi-valued 3×3 filter kernel has been shown and described, other types may be used for accomplishing substantially similar objective of the invention. In summary, the scope of the invention should not be restricted to the specific example embodiments disclosed herein, and all modifications that are readily suggested to those of ordinary skill in the art should be included within the spirit and purview of this application and scope of the appended claims. 

What is claimed is:
 1. A device comprising: a bus; an input interface coupled to the bus for receiving a two-dimensional (2-D) symbol graphically representing a Chinese poetry or verse, the 2-D symbol being a matrix of N×N pixels of K-bit data, the matrix being divided into M×M sub-matrices each containing (N/M)×(N/M) pixels, said each of the sub-matrices containing an ideogram created by embedding fonts of all characters contained in a corresponding phrase of the Chinese poetry or verse, where K, N and M are positive integers, and N is a multiple of M; and at least one cellular neural networks (CNN) based integrated circuit (IC) operatively coupled to the bus and being configured for processing and understanding semantic meaning of the Chinese poetry or verse contained in the 2-D symbol using an image classification technique, each of the at least one CNN based IC comprising a plurality of CNN processing engines operatively coupled to at least one input/output data bus, the plurality of CNN processing engines being connected in a loop with a clock-skew circuit, each CNN processing engine includes: a CNN processing block configured for simultaneously obtaining convolutional operations results using a corresponding portion of the 2-D symbol and pre-trained filter coefficients in a deep learning model; a first set of memory buffers operatively coupled to the CNN processing block for storing the corresponding portion; and a second set of memory buffers operative coupled to the CNN processing block for storing the pre-trained filter coefficients.
 2. The device of claim 1, wherein the deep learning model contains a plurality of ordered convolutional layers.
 3. The device of claim 2, wherein the deep learning model further contains layers of pooling and activation operations.
 4. The device of claim 1, wherein the pre-trained filter coefficients are obtained by training the deep learning model using a training dataset containing labeled 2-D symbols that graphically represent classical Chinese poems or verses.
 5. The device of claim 4, wherein each of the labeled 2-D symbols in the training dataset is assigned with a sentiment.
 6. The device of claim 5, wherein the sentiment is in form of a Chinese expression reflecting the semantic meaning of said each classical Chinese poem or verse.
 7. The device of claim 5, wherein the sentiment is in form of a picture reflecting the semantic meaning of said each classical Chinese poem or verse.
 8. The device of claim 7, wherein each classical Chinese poem or verse comprises multiple Chinese characters in a particular format in terms of how many phrases and how many characters per phrase.
 9. The device of claim 1 further comprises a display as an output interface.
 10. The device of claim 9 further comprises a processing unit as a controller.
 11. The device of claim 9 further comprises a memory.
 12. The device of claim 1, wherein the input interface comprises a camera.
 13. The device of claim 1, wherein the input interface comprises a touch screen display.
 14. A device comprising: a bus; an input interface coupled to the bus for receiving a two-dimensional (2-D) symbol graphically representing a Chinese poetry or verse, the 2-D symbol being a matrix of N×N pixels of K-bit data, the matrix being divided into M×M sub-matrices each containing (N/M)×(N/M) pixels, said each of the sub-matrices containing a pictogram representing an artistic perception of each sentence of the Chinese poetry or verse, where K, N and M are positive integers, and N is a multiple of M; and at least one cellular neural networks (CNN) based integrated circuit (IC) operatively coupled to the bus and being configured for processing and understanding semantic meaning of the Chinese poetry or verse within the 2-D symbol using an image classification technique, each of the at least one CNN based IC comprising a plurality of CNN processing engines operatively coupled to at least one input/output data bus, the plurality of CNN processing engines being connected in a loop with a clock-skew circuit, each CNN processing engine includes: a CNN processing block configured for simultaneously obtaining convolutional operations results using a corresponding portion of the 2-D symbol and pre-trained filter coefficients in a deep learning model; a first set of memory buffers operatively coupled to the CNN processing block for storing the corresponding portion; and a second set of memory buffers operative coupled to the CNN processing block for storing the pre-trained filter coefficients.
 15. The device of claim 14, wherein the pictogram comprises an encoded graphical symbol.
 16. The device of claim 14, wherein the pictogram comprises a graphical image of the Chinese poetry or verse. 